Research program
Problems money alone
does not solve.
For companies and people working with a fixed budget, we study what it takes to actually run AI.
The problem
Hardware
Most AI models run on datacenter GPUs.
The machines companies and people actually have sit far below that.
Numbers
Published A standard test used to score a model. Change the conditions of the test and the score changes with them. always look good.
There is rarely a way to check whether the numbers came from the same conditions.
Language
Non-English languages get less attention.
Most tooling is built around English. Korean spends more The chunks of text an AI reads and writes. Most AI services bill you by how many chunks you use. on the same sentence, and Hindi has far fewer public numbers to compare against.
Rights
Labeled open source or The model files themselves are published, so anyone can download them and run the model on their own machine. does not mean you can use it as-is.
Whether you can actually run it depends on the license and the provenance.
Approach
We go where the budget does not point
Most AI progress is bought: more parameters, more compute, more data. The frontier moves in whatever direction the largest budget points. Problems in that direction get solved eventually if you wait. We work only on the ones that waiting does not solve.
Finish on the device
No GPU. No network. Run end to end on hardware people already own.
Keep every result
One number per test set. Wins, ties, and losses all stay public.
Start with language
Measure token cost and performance again in Hindi and Korean. Recheck what worked in English.
Licensing
What we can promise
We bind our own work. We do not warranty the upstream.
We bind
- An open license that lets anyone use, change, and sell what they build on it, as long as the credit notice stays. or MIT bases only
- No training on commercial API outputs
- Releases under Apache-2.0, as-is
We do not vouch
- Someone else's model or data that we started from, rather than made ourselves. data we never saw
- Anything beyond license + The datasheet that ships with a model: what it was built from, what it is for, and where it fails. + stated provenance
Inside a company
Production as the agenda
Constraints from shipping products set the agenda. Artifacts go back into the products.
Products
Real users, real devices
Constraints
What spending cannot buy past
Lab artifacts
Open methods under pressure
Never leaves: customer data, internal costs, pricing, or terms - paper, project, or benchmark.
Leadership
Janghoon Lee
Head of Redrob Labs, Redrob CTO
Led by one person, but built with 50+ Redrob engineers across Seoul, India, and the US. This is where the foundations of the tools in the Redrob platform get built.
Our best results kept coming from constraints. Trying to buy past them never helped.
Updated August 7, 2026